In an increasingly digital and interconnected world, there has been a paradigm shift in the way consumers make purchase decisions and shop for various products. For example, technological advancements have remarkably contributed to facilitate ease of shopping through different channels and to provide accessibility to various product vendors over Internet. Further, the online or retail vendors may provide consumers with various innovative solutions so as to enable consumers in choosing right products based on their requirements. These innovative solutions may include artificial intelligence (AI) or virtual reality (VR) based human modeling for clothes size selection, customer pool review based product recommendation over search results, personalized product recommendation based on the user browsing history, and so forth. However, despite such advance solutions, the consumers may still fail to get the right products as per their requirements. Additionally, in some cases, the purchased product may fail before its operational life cycle due to different operational environment at the consumer's location than that deemed appropriate for working of the product.
Currently, the product manufacturers depend on their product production, product sales, and product servicing team to generate product information data through sales data, servicing data, customer feedback, online surveys, or the like. However, none of the feedback mechanisms provide accurate and reliable data regarding functionality of the products installed at the consumers' premises so as to validate product failures, expand product portfolio, provide customized product recommendations, and so forth.